A Framework for Evaluating Effects of Channel Prediction Inaccuracy on the Performance of Channel Adaptive Techniques

作者: Adam Wolisz , Horst Miesmer , Holger Karl , Ana Aguiar

DOI:

关键词: State (computer science)Performance resultsCommunication channelTraffic generation modelComputer scienceWirelessAlgorithmThroughputData miningTransmission (telecommunications)Channel models

摘要: Abstract—Adapting transmission parameters to the future channel state is an appealing approach improve efficiency in wireless communication. Adaptation requires predicting state. Current channel-adaptive techniques as- sume perfect prediction. In this paper, we claim that neglect- ing prediction error leads poor performance results, possibly even worse than without at all. We have developed a simulation framework which allows us inves- tigate effects of on achieved perfor- mance results (e. g., throughput) independently predic- tion algorithm by introducing models for error. Furthermore, environment offers flexible in- terfaces allow replace components model, like traffic generator, model. To substantiate our claim, investigated two schedulers, showing has be considered.

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